RobloxPotatoes/aimbot.py
2023-01-21 13:51:20 -06:00

83 lines
2.8 KiB
Python

import tensorflow as tf
import tensorflow_hub as hub
import numpy as np
import pyautogui
import win32api, win32con, win32gui
import cv2
import math
import time
import os
dirname = os.path.dirname(__file__)
detector = tf.saved_model.load(dirname)
size_scale = 3
while True:
# Get rect of Window
hwnd = win32gui.FindWindow(None, 'Roblox')
#hwnd = win32gui.FindWindow("UnrealWindow", None) # Fortnite
rect = win32gui.GetWindowRect(hwnd)
region = rect[0], rect[1], rect[2] - rect[0], rect[3] - rect[1]
# Get image of screen
ori_img = np.array(pyautogui.screenshot(region=region))
ori_img = cv2.resize(ori_img, (ori_img.shape[1] // size_scale, ori_img.shape[0] // size_scale))
image = np.expand_dims(ori_img, 0)
img_w, img_h = image.shape[2], image.shape[1]
# Detection
result = detector(image)
result = {key:value.numpy() for key,value in result.items()}
boxes = result['detection_boxes'][0]
scores = result['detection_scores'][0]
classes = result['detection_classes'][0]
# Check every detected object
detected_boxes = []
for i, box in enumerate(boxes):
# Choose only person(class:1)
if classes[i] == 1 and scores[i] >= 0.5:
ymin, xmin, ymax, xmax = tuple(box)
if ymin > 0.5 and ymax > 0.8: # CS:Go
#if int(xmin * img_w * 3) < 450: # Fortnite
continue
left, right, top, bottom = int(xmin * img_w), int(xmax * img_w), int(ymin * img_h), int(ymax * img_h)
detected_boxes.append((left, right, top, bottom))
#cv2.rectangle(ori_img, (left, top), (right, bottom), (255, 255, 0), 2)
print("Detected:", len(detected_boxes))
# Check Closest
if len(detected_boxes) >= 1:
min = 99999
at = 0
centers = []
for i, box in enumerate(detected_boxes):
x1, x2, y1, y2 = box
c_x = ((x2 - x1) / 2) + x1
c_y = ((y2 - y1) / 2) + y1
centers.append((c_x, c_y))
dist = math.sqrt(math.pow(img_w/2 - c_x, 2) + math.pow(img_h/2 - c_y, 2))
if dist < min:
min = dist
at = i
# Pixel difference between crosshair(center) and the closest object
x = centers[at][0] - img_w/2
y = centers[at][1] - img_h/2 - (detected_boxes[at][3] - detected_boxes[at][2]) * 0.45
# Move mouse and shoot
scale = 1.7 * size_scale
x = int(x * scale)
y = int(y * scale)
win32api.mouse_event(win32con.MOUSEEVENTF_MOVE, x, y, 0, 0)
time.sleep(0.05)
win32api.mouse_event(win32con.MOUSEEVENTF_LEFTDOWN, x, y, 0, 0)
time.sleep(0.1)
win32api.mouse_event(win32con.MOUSEEVENTF_LEFTUP, x, y, 0, 0)
#ori_img = cv2.cvtColor(ori_img, cv2.COLOR_BGR2RGB)
#cv2.imshow("ori_img", ori_img)
#cv2.waitKey(1)
time.sleep(0.1)